Olivio F. Donati
University of Zurich
Internal medicineRadiologyPathologyMagnetic resonance imagingPerfusionArtificial intelligenceCardiologyRetrospective cohort studyPositron emission tomographyBiopsyProstateCoronary artery diseaseProstatectomyNeuroradiologyProstate cancerNuclear medicineComputer scienceImage qualityReceiver operating characteristicMedicineSegmentation
101Publications
29H-index
2,275Citations
Publications 107
Newest
#1Sara Sangalli (ETH Zurich)H-Index: 2
#2Ertunc Erdil (ETH Zurich)H-Index: 10
Last. Ender Konukoglu (ETH Zurich)H-Index: 39
view all 5 authors...
#1Daniela A. Ferraro (USP: University of São Paulo)H-Index: 9
#2Riccardo Laudicella (UNIME: University of Messina)H-Index: 2
Last. Irene A. Burger (UZH: University of Zurich)H-Index: 30
view all 13 authors...
Prostate-specific membrane antigen (PSMA)-targeted PET is increasingly used for staging prostate cancer (PCa) with high accuracy to detect significant PCa (sigPCa). [68 Ga]PSMA-11 PET/MRI-guided biopsy showed promising results but also persisting limitation of sampling error, due to impaired image fusion. We aimed to assess the possibility of intraoperative quantification of [18F]PSMA-1007 PET/CT uptake in core biopsies as an instant confirmation for accurate lesion sampling. In this IRB-approve...
Source
#1Stephan Skawran (UZH: University of Zurich)H-Index: 3
#2Vanessa Sanchez (UZH: University of Zurich)
Last. Olivio F. Donati (UZH: University of Zurich)H-Index: 29
view all 8 authors...
PURPOSE Comparing mpMRI and 68Ga-PSMA-PET/MRI in primary staging of PCa and investigating the value of quantitative mpMRI-measurements for prediction of extracapsular extension and N-metastases. METHODS Patients with PCa undergoing 68Ga-PSMA-PET/MRI and mpMRI during January 2016 to February 2019 were retrospectively included. Two readers each on 68Ga-PSMA-PET/MRI or mpMRI rated extraprostatic extension (≥T3) and regional lymph-node-metastasis (N1) on a Likert-scale. A fifth reader measured tumor...
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#1Andreas M. Hötker (UZH: University of Zurich)H-Index: 8
#2Raffaele Da Mutten (UZH: University of Zurich)
Last. Olivio F. Donati (UZH: University of Zurich)H-Index: 29
view all 5 authors...
OBJECTIVES To develop and validate an artificial intelligence algorithm to decide on the necessity of dynamic contrast-enhanced sequences (DCE) in prostate MRI. METHODS This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A convolutional neural network (CNN) was developed on 300 prostate MRI examinations. Consensus of two expert readers on the necessity of DCE acted as reference standard. The CNN was validated in a separate coh...
Source
#1Andreas M. Hötker (UZH: University of Zurich)H-Index: 8
#2Olivio F. DonatiH-Index: 29
Clinical/methodological issue null The detection of clinically significant prostate cancers while simultaneously avoiding over-diagnosing tumors with low malignant potential is a challenge in clinical practice. null Standard radiological methods null Multiparametric prostate magnetic resonance imaging (MRI) in accordance with the Prostate Imaging Reporting and Data System (PI-RADS) guidelines is accepted as standard-of-care with both urologists and radiologists. null Methodological innovations n...
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#1André Euler (UZH: University of Zurich)H-Index: 5
#2Matthias ZadoryH-Index: 1
Last. Olivio F. DonatiH-Index: 29
view all 7 authors...
Purpose The aims of this study were to assess if kidney tissue surrogates (KTSs) are superior to distilled water-iodine solutions in the emulation of energy-dependent computed tomography (CT) attenuation characteristics of renal parenchyma and to estimate attenuation thresholds for definite lesion enhancement for low-kV single-energy and low-keV dual-energy virtual monoenergetic imaging. Methods A water-filled phantom (diameter, 30 cm) with multiple vials was imaged on a dual-source dual-energy ...
Source
#1Cynthia Schmidt (UZH: University of Zurich)H-Index: 2
#2Andreas M. Hötker (UZH: University of Zurich)H-Index: 8
Last. Borna K. Barth (UZH: University of Zurich)H-Index: 11
view all 6 authors...
BACKGROUND Bowel preparation before multiparametric MRI (mpMRI) of the prostate is performed widely, despite contradictory or no evidence for efficacy. PURPOSE To investigate the value of hyoscine N-butylbromide (HBB), microenema (ME) and 'dietary restrictions' (DR) for artifact reduction and image quality (IQ) in mpMRI of the prostate. STUDY TYPE Retrospective. POPULATION Between 10/2018 and 02/2020 treatment-naive men (median age, 64.9; range 39.8-87.3) who underwent mpMRI of the prostate were...
Source
#1Daniela A. Ferraro (USP: University of São Paulo)H-Index: 9
#2Anton S. Becker (UZH: University of Zurich)H-Index: 23
Last. Irene A. Burger (UZH: University of Zurich)H-Index: 30
view all 15 authors...
PURPOSE Ultrasound-guided biopsy (US biopsy) with 10-12 cores has a suboptimal sensitivity for clinically significant prostate cancer (sigPCa). If US biopsy is negative, magnetic resonance imaging (MRI)-guided biopsy is recommended, despite a low specificity for lesions with score 3-5 on Prostate Imaging Reporting and Data System (PIRADS). Screening and biopsy guidance using an imaging modality with high accuracy could reduce the number of unnecessary biopsies, reducing side effects. The aim of ...
Source
#1Sara Sangalli (ETH Zurich)H-Index: 2
#2Ertunc ErdilH-Index: 10
Last. Ender KonukogluH-Index: 39
view all 5 authors...
Deep neural networks (DNNs) are notorious for making more mistakes for the classes that have substantially fewer samples than the others during training. Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases (e.g., cancer) where misclassifications can have severe consequences. Not to miss such cases, binary classifiers need to be operated at high True Positive Rates (TPR) by setting a ...
#1Sara Sangalli (ETH Zurich)H-Index: 2
#2Ertunc ErdilH-Index: 10
Last. Ender KonukogluH-Index: 39
view all 5 authors...
Deep neural networks (DNNs) are notorious for making more mistakes for the classes that have substantially fewer samples than the others during training. Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases (e.g., cancer) where misclassifications can have severe consequences. Not to miss such cases, binary classifiers need to be operated at high True Positive Rates (TPR) by setting a ...
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